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Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix

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  • Kourtis, Apostolos
  • Dotsis, George
  • Markellos, Raphael N.

Abstract

The estimation of the inverse covariance matrix plays a crucial role in optimal portfolio choice. We propose a new estimation framework that focuses on enhancing portfolio performance. The framework applies the statistical methodology of shrinkage directly to the inverse covariance matrix using two non-parametric methods. The first minimises the out-of-sample portfolio variance while the second aims to increase out-of-sample risk-adjusted returns. We apply the resulting estimators to compute the minimum variance portfolio weights and obtain a set of new portfolio strategies. These strategies have an intuitive form which allows us to extend our framework to account for short-sale constraints, transaction costs and singular covariance matrices. A comparative empirical analysis against several strategies from the literature shows that the new strategies often offer higher risk-adjusted returns and lower levels of risk.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 36 (2012)
Issue (Month): 9 ()
Pages: 2522-2531

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Handle: RePEc:eee:jbfina:v:36:y:2012:i:9:p:2522-2531

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Web page: http://www.elsevier.com/locate/jbf

Related research

Keywords: Portfolio optimisation; Inverse covariance matrix; Estimation risk; Shrinkage;

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References

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Citations

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Cited by:
  1. Dimitrios D. Thomakos & Fotis Papailias, 2013. "Covariance Averaging for Improved Estimation and Portfolio Allocation," Working Paper Series 66_13, The Rimini Centre for Economic Analysis.
  2. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J., 2013. "Size matters: Optimal calibration of shrinkage estimators for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3018-3034.
  3. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.
  4. Vahe Avagyan & Andrés M. Alonso & Francisco J. Nogales, 2014. "Improving the graphical lasso estimation for the precision matrix through roots ot the sample convariance matrix," Statistics and Econometrics Working Papers ws141208, Universidad Carlos III, Departamento de Estadística y Econometría.

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